Applying Artificial neural network into kaggle's house prices data set gave bad predicted values
Is it because the number of hidden layers with its input? Or because there is few data to train (1460 rows of train set) and the neural network needs more than that? Or is it because of the number of neurons in each layer?


I think you are onto something here. You have over a thousand nodes, almost as many as training samples you have. In my experience, the network will have no trouble overfitting, given you train long enough. If you dont train long enough on the other hand, your network will probably not learn anything.

I would try with a smaller network, so you "force" it to learn something, instead of memorizing.